Adaptive smart antenna processing may be used in a communication station (e.g., a base station) equipped with multiple antennas to either reject interference when communicating from a subscriber unit to the communication station (i.e., on the uplink) or to deliver power in a spatially or spatio-temporally selective manner when communicating from the communication station to a subscriber unit (i.e., on the downlink). With smart antenna communication systems that use linear spatial processing for the adaptive smart antenna processing, during uplink communications, one applies amplitude and phase adjustments, typically but not necessarily in baseband to each of the signals received at the antenna array elements to select (i.e., preferentially receive) the signals of interest while minimizing any signals or noise not of interest—that is, the interference. Such baseband amplitude and phase adjustment can be described by a complex valued weight, the receive weight, and the receive weights for all elements of the array can be described by a complex valued vector, the receive weight vector. Similarly, the downlink signal is processed by adjusting the amplitude and phase of the baseband signals that are transmitted by each of the antennas of the antenna array. Such amplitude and phase control can be described by a complex valued weight, the transmit weight, and the weights for all elements of the array by a complex valued vector, the transmit weight vector. In some systems, the receive (and/or transmit) weights include temporal processing, and in such cases, the receive (and/or transmit) weights may be functions of frequency and applied in the frequency domain or, equivalently, functions of time applied as convolution kernels. Alternatively, each convolution kernel, if for sampled signals, may itself be described by a set of complex numbers, so that the vector of convolution kernels may be re-written as a complex values weight vector, which, for the case of there being M antennas and each kernel having K entries, would be a vector of KM entries.
Many methods are known for performing interference rejection or selective power delivery. Examples include least-squares beamforming and zero-forcing beamforming. Selective power delivery must balance competing goals. In general, the power delivered to one remote user cannot be simultaneously maximized while the power delivered to another remote user is minimized. More generally, if several remote users require power minimization (i.e., nulling), the relative power delivered to each must be traded off. This tradeoff can be based on a number of factors. For example, for a given interferer (which might be a co-channel user), a deeper null (i.e., decreased transmitted power aimed at an interferer during transmission or decreased sensitivity to signals transmitted from the interferer during reception) may be required for a remote user co-participating in the spatial channel established at the base station than for a remote user communicating with a different base station.
A disadvantage of known methods, such as least-squares and zero-forcing, is their inability to flexibly perform this tradeoff. Zero-forcing methods attempt to direct perfect nulls towards all interferers, regardless of the power delivered to the desired remote user. Least-squares methods minimize a cost function which, when the uplink strategies are used to determine downlink strategies, on the downlink directs strongest nulls to remote users who were received most strongly on the uplink. Neither zero-forcing nor least-squares behavior may be appropriate for some systems.
A particular case where least-squares nulling behavior is undesirable occurs when subscriber units are subject to the “near/far problem.” In the near/far problem, one subscriber unit, say one denoted SU1, is far from the base station while a second subscriber unit, say one denoted SU2, is close. When the downlink weight vector, denoted w1, is computed for SU1, signals from SU2 are seen as interference and a null is formed towards SU2. The depth of the null in the direction of SU2 is limited by noise and other factors, including the possibility that the transmitted powers of subscriber units are adjusted such that equal power levels are received by the base station. If high power is used to reach SU1 during base station transmit, then excessive signal levels leaking “through” in the imperfect null can disturb SU2. A deeper null towards SU2 is therefore desirable.
A disadvantage of zero-forcing methods is the need for the base station to have complete knowledge of all remote user and interferer spatial signatures (or spatio-temporal signatures). The receive spatial signature and the receive spatio-temporal signature characterizes how the base station array receives signals from a particular subscriber unit in the absence of any interference or other subscriber units. The transmit spatial signature and the transmit spatio-temporal signature of a particular remote user characterizes how the remote user receives signals from the base station in the absence of any interference. See U.S. Pat. No. 5,592,490 entitled SPECTRALLY EFFICIENT HIGH CAPACITY WIRELESS COMMUNICATION SYSTEMS, to Barratt et al., assigned to the assignee of the present invention and incorporated herein by reference, and U.S. Pat. No. 5,828,658 entitled SPECTRALLY EFFICIENT HIGH CAPACITY WIRELESS COMMUNICATION SYSTEMS WITH SPATIO-TEMPORAL PROCESSING, to Ottersten et al., also assigned to the assignee of the present invention and incorporated herein by reference, for spatial processing and spatio-temporal processing methods that use spatial and spatio-temporal signatures. Note that because a signature may be a spatial signature or a spatio-temporal signature, depending on whether the smart antenna processing is spatial or spatio-temporal, the term signature will be used herein, and whether the signature is spatial or spatio-temporal will depend on whether the processing is spatial or spatio-temporal, and whether the signature is a transmit or a receive signature will depend on the context, and which signature will be clear to those of ordinary skill in the art from the context.
Determining all spatial signatures of all remote users and all interferers may often be impossible to carry out accurately if any of the interferers are weak or have a signal structure that is a priori unknown, or because of computational power limitations.
In the uplink direction, increasing the depth of a null (i.e, decreasing sensitivity to signals received from a particular interferer) is desirable when the uplink strategy, for example the uplink weight vector, computed from a previous burst is used in predictive mode, e.g., for new data. When uplink remote users are executing power control, for example to control received power at the base station or to reduce transmit power during periods of voice or data inactivity, their uplink power may vary widely from burst to burst. Similar effects occur in fading environments. Thus, if a least-squares approach is used, the null depth obtained from a strategy computed for the previous burst may be inappropriate for the current burst.
Thus there is a need in the art for a flexible method for directing precise and deep nulls on the uplink or downlink direction. There also is a need in the art for a method and apparatus for estimating one or more signatures in the direction of one or more interferers to use, for example, for such null deepening. There also is a need for a method for using a signature estimate of an interferer for directing precise and deep nulls in the direction of the interferer. There also is a need in the art for a method for directing precise and deep nulls in the direction of one or more interferers while substantially maintaining the other nulling and gain patterns of a provided adaptive smart antenna processing strategy.